Effect of Ionomer on Barrier and Mechanical Properties of PET/Organoclay Nanocomposites Prepared by Melt Compounding
Bibliographic record
Abstract
Abstract Poly(ethylene terephthalate)/organo-modified-montmorillonite (o-MMT) nanocomposites were prepared via melt compounding. A polyester ionomer was used as a compatibilizer to increase the interaction between the nanoclay and PET. The nominal o-MMT content was 2 wt.% and the ionomer/organoclay (mass ratio) was 3:1. The samples were characterized by WAXD, SEM, TEM, TGA, rheometry, DSC, O2 permeation and tensile testing. It was found that the addition of the ionomer improved the dispersion of the nanoclay particles in the PET matrix, leading to an exfoliated structure for the samples prepared by twin screw extrusion and by an internal mixer (Brabender). This was confirmed by larger complex viscosity and storage modulus at low frequency for molten samples. However, a subsequent processing using single screw extrusion to produce films resulted in thermal degradation of the organo-modifier of the clay and collapse of the gallery spacing. DSC results revealed that the cold crystallization temperature of nanocomposites-based films decreased and the melt crystallization temperature increased with the introduction of the organoclay, due to the strong heterogeneous nucleation effect of the clay particles. The tensile modulus of extruded films increased, while the yield strength remained constant with the incorporation of the organoclay. The oxygen permeability of PET-ionomer nanocomposites decreased as compared with samples containing no ionomer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".